221 related articles for article (PubMed ID: 28150713)
1. Discovering missing reactions of metabolic networks by using gene co-expression data.
Hosseini Z; Marashi SA
Sci Rep; 2017 Feb; 7():41774. PubMed ID: 28150713
[TBL] [Abstract][Full Text] [Related]
2. Meneco, a Topology-Based Gap-Filling Tool Applicable to Degraded Genome-Wide Metabolic Networks.
Prigent S; Frioux C; Dittami SM; Thiele S; Larhlimi A; Collet G; Gutknecht F; Got J; Eveillard D; Bourdon J; Plewniak F; Tonon T; Siegel A
PLoS Comput Biol; 2017 Jan; 13(1):e1005276. PubMed ID: 28129330
[TBL] [Abstract][Full Text] [Related]
3. Flux coupling analysis of metabolic networks is sensitive to missing reactions.
Marashi SA; Bockmayr A
Biosystems; 2011 Jan; 103(1):57-66. PubMed ID: 20888889
[TBL] [Abstract][Full Text] [Related]
4. Sequence-based Network Completion Reveals the Integrality of Missing Reactions in Metabolic Networks.
Krumholz EW; Libourel IG
J Biol Chem; 2015 Jul; 290(31):19197-207. PubMed ID: 26041773
[TBL] [Abstract][Full Text] [Related]
5. A computational method using differential gene expression to predict altered metabolism of multicellular organisms.
Zhu L; Zheng H; Hu X; Xu Y
Mol Biosyst; 2017 Oct; 13(11):2418-2427. PubMed ID: 28972214
[TBL] [Abstract][Full Text] [Related]
6. An algorithm for the reduction of genome-scale metabolic network models to meaningful core models.
Erdrich P; Steuer R; Klamt S
BMC Syst Biol; 2015 Aug; 9():48. PubMed ID: 26286864
[TBL] [Abstract][Full Text] [Related]
7. Efficiently gap-filling reaction networks.
Latendresse M
BMC Bioinformatics; 2014 Jun; 15():225. PubMed ID: 24972703
[TBL] [Abstract][Full Text] [Related]
8. Advances in gap-filling genome-scale metabolic models and model-driven experiments lead to novel metabolic discoveries.
Pan S; Reed JL
Curr Opin Biotechnol; 2018 Jun; 51():103-108. PubMed ID: 29278837
[TBL] [Abstract][Full Text] [Related]
9. On correlated reaction sets and coupled reaction sets in metabolic networks.
Marashi SA; Hosseini Z
J Bioinform Comput Biol; 2015 Aug; 13(4):1571003. PubMed ID: 25747383
[TBL] [Abstract][Full Text] [Related]
10. FCDECOMP: decomposition of metabolic networks based on flux coupling relations.
Rezvan A; Marashi SA; Eslahchi C
J Bioinform Comput Biol; 2014 Oct; 12(5):1450028. PubMed ID: 25362842
[TBL] [Abstract][Full Text] [Related]
11. Hierarchical organization of fluxes in Escherichia coli metabolic network: using flux coupling analysis for understanding the physiological properties of metabolic genes.
Hosseini Z; Marashi SA
Gene; 2015 May; 561(2):199-208. PubMed ID: 25688882
[TBL] [Abstract][Full Text] [Related]
12. On flux coupling analysis of metabolic subsystems.
Marashi SA; David L; Bockmayr A
J Theor Biol; 2012 Jun; 302():62-9. PubMed ID: 22406036
[TBL] [Abstract][Full Text] [Related]
13. Systematic assignment of thermodynamic constraints in metabolic network models.
Kümmel A; Panke S; Heinemann M
BMC Bioinformatics; 2006 Nov; 7():512. PubMed ID: 17123434
[TBL] [Abstract][Full Text] [Related]
14. A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.
Röhl A; Bockmayr A
BMC Bioinformatics; 2017 Jan; 18(1):2. PubMed ID: 28049424
[TBL] [Abstract][Full Text] [Related]
15. Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.
Hamilton JJ; Reed JL
PLoS One; 2012; 7(4):e34670. PubMed ID: 22666308
[TBL] [Abstract][Full Text] [Related]
16. Estimating the size of the solution space of metabolic networks.
Braunstein A; Mulet R; Pagnani A
BMC Bioinformatics; 2008 May; 9():240. PubMed ID: 18489757
[TBL] [Abstract][Full Text] [Related]
17. Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints.
Ren S; Zeng B; Qian X
BMC Bioinformatics; 2013; 14 Suppl 2(Suppl 2):S17. PubMed ID: 23368729
[TBL] [Abstract][Full Text] [Related]
18. Gap-filling analysis of the iJO1366 Escherichia coli metabolic network reconstruction for discovery of metabolic functions.
Orth JD; Palsson B
BMC Syst Biol; 2012 May; 6():30. PubMed ID: 22548736
[TBL] [Abstract][Full Text] [Related]
19. Computational Prediction of Synthetic Lethals in Genome-Scale Metabolic Models Using Fast-SL.
Raman K; Pratapa A; Mohite O; Balachandran S
Methods Mol Biol; 2018; 1716():315-336. PubMed ID: 29222760
[TBL] [Abstract][Full Text] [Related]
20. MIRAGE: a functional genomics-based approach for metabolic network model reconstruction and its application to cyanobacteria networks.
Vitkin E; Shlomi T
Genome Biol; 2012 Nov; 13(11):R111. PubMed ID: 23194418
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]